



'''simple docstring'''






import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase:   Tuple					=		1_6
lowerCAmelCase:   List[str]					=		3_2
def  lowerCamelCase__			(		_A					,     _A = 16							):
  a		:   str	  =	AutoTokenizer.from_pretrained('bert-base-cased'							)
  a		:   int	  =	load_dataset('glue'					,     'mrpc'							)

  def tokenize_function(_A							):
    # max_length=None => use the model max length (it's actually the default)
    a		:   int	  =	tokenizer(examples['sentence1']					,     examples['sentence2']					,     truncation=_A					,     max_length=_A							)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    a		:   int	  =	datasets.map(
        _A					,     batched=_A					,     remove_columns=['idx', 'sentence1', 'sentence2']					,     )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  a		:   Tuple	  =	tokenized_datasets.rename_column('label'					,     'labels'							)

  def collate_fn(_A							):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a		:   Union[str, Any]	  =	128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      a		:   Optional[Any]	  =	16
    elif accelerator.mixed_precision != "no":
      a		:   Dict	  =	8
    else:
      a		:   Optional[Any]	  =	None

    return tokenizer.pad(
        _A					,     padding='longest'					,     max_length=_A					,     pad_to_multiple_of=_A					,     return_tensors='pt'					,     )

  # Instantiate dataloaders.
  a		:   List[Any]	  =	DataLoader(
      tokenized_datasets['train']					,     shuffle=_A					,     collate_fn=_A					,     batch_size=_A					,     drop_last=_A							)
  a		:   Optional[int]	  =	DataLoader(
      tokenized_datasets['validation']					,     shuffle=_A					,     collate_fn=_A					,     batch_size=_A					,     drop_last=(accelerator.mixed_precision == 'fp8')					,     )

  return train_dataloader, eval_dataloader
def  lowerCamelCase__			(		_A					,     _A							):
  # Initialize accelerator
  a		:   Union[str, Any]	  =	Accelerator(cpu=args.cpu					,     mixed_precision=args.mixed_precision							)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a		:   int	  =	config['lr']
  a		:   Tuple	  =	int(config['num_epochs']							)
  a		:   Dict	  =	int(config['seed']							)
  a		:   str	  =	int(config['batch_size']							)

  a		:   Tuple	  =	evaluate.load('glue'					,     'mrpc'							)

  # If the batch size is too big we use gradient accumulation
  a		:   List[Any]	  =	1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a		:   Union[str, Any]	  =	batch_size // MAX_GPU_BATCH_SIZE
    a		:   Union[str, Any]	  =	MAX_GPU_BATCH_SIZE

  set_seed(_A							)
  a  ,      a		:   str	  =	get_dataloaders(_A					,     _A							)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a		:   Any	  =	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'					,     return_dict=_A							)

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  a		:   Optional[int]	  =	model.to(accelerator.device							)
  # Instantiate optimizer
  a		:   Union[str, Any]	  =	AdamW(params=model.parameters()					,     lr=_A							)

  # Instantiate scheduler
  a		:   Tuple	  =	get_linear_schedule_with_warmup(
      optimizer=_A					,     num_warmup_steps=100					,     num_training_steps=(len(_A							) * num_epochs) // gradient_accumulation_steps					,     )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.

  a  ,      a  ,      a  ,      a  ,      a		:   Union[str, Any]	  =	accelerator.prepare(
      _A					,     _A					,     _A					,     _A					,     _A							)

  # Now we train the model
  for epoch in range(_A							):
    model.train()
    for step, batch in enumerate(_A							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      a		:   Dict	  =	model(**_A							)
      a		:   Tuple	  =	outputs.loss
      a		:   Optional[int]	  =	loss / gradient_accumulation_steps
      accelerator.backward(_A							)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    for step, batch in enumerate(_A							):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device							)
      with torch.no_grad():
        a		:   Union[str, Any]	  =	model(**_A							)
      a		:   int	  =	outputs.logits.argmax(dim=-1							)
      a  ,      a		:   Any	  =	accelerator.gather_for_metrics((predictions, batch['labels'])							)
      metric.add_batch(
          predictions=_A					,     references=_A					,     )

    a		:   List[str]	  =	metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f"""epoch {epoch}:"""					,     _A							)
def  lowerCamelCase__			(		):
  a		:   List[str]	  =	argparse.ArgumentParser(description='Simple example of training script.'							)
  parser.add_argument(
      '--mixed_precision'					,     type=_A					,     default=_A					,     choices=['no', 'fp16', 'bf16', 'fp8']					,     help='Whether to use mixed precision. Choose'
      'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
      'and an Nvidia Ampere GPU.'					,     )
  parser.add_argument('--cpu'					,     action='store_true'					,     help='If passed, will train on the CPU.'							)
  a		:   Union[str, Any]	  =	parser.parse_args()
  a		:   str	  =	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(_A					,     _A							)


if __name__ == "__main__":
     main()




'''simple docstring'''






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class 				a__:







    def __init__( self					:    List[Any]		,     __snake_case					:    str      ):
      if isinstance(__snake_case		,     __snake_case      ):
        # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
        # modified it, it will not be accepted here again, since `auto` values would have been overridden
        a		:   Union[str, Any]	  =	deepcopy(__snake_case      )
      elif os.path.exists(__snake_case      ):
        with io.open(__snake_case		,     'r'		,     encoding='utf-8'      ) as f:
          a		:   List[str]	  =	json.load(__snake_case      )
      else:
        try:
          a		:   str	  =	baseaa.urlsafe_baadecode(__snake_case      ).decode('utf-8'      )
          a		:   Union[str, Any]	  =	json.loads(__snake_case      )
        except (UnicodeDecodeError, AttributeError, ValueError):
          raise ValueError(
              F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""      )

      a		:   Optional[int]	  =	config

      self.set_stage_and_offload()







    def      lowercase_			( self					:    Any      ):
      # zero stage - this is done as early as possible, before model is created, to allow
      # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
      # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
      a		:   List[str]	  =	self.get_value('zero_optimization.stage'		,     -1      )

      # offload
      a		:   Union[str, Any]	  =	False
      if self.is_zeroa() or self.is_zeroa():
        a		:   Any	  =	set(['cpu', 'nvme']      )
        a		:   Tuple	  =	set(
            [
                self.get_value('zero_optimization.offload_optimizer.device'      ),
                self.get_value('zero_optimization.offload_param.device'      ),
            ]      )
        if len(offload_devices & offload_devices_valid      ) > 0:
          a		:   Optional[int]	  =	True







    def      lowercase_			( self					:    str		,     __snake_case					:    Optional[Any]      ):
      a		:   Any	  =	self.config

      # find the config node of interest if it exists
      a		:   Dict	  =	ds_key_long.split('.'      )
      a		:   str	  =	nodes.pop()
      for node in nodes:
        a		:   Tuple	  =	config.get(__snake_case      )
        if config is None:
          return None, ds_key

      return config, ds_key







    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[int]		,     __snake_case					:    List[str]=None      ):
      a  ,      a		:   int	  =	self.find_config_node(__snake_case      )
      if config is None:
        return default
      return config.get(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Dict		,     __snake_case					:    List[str]		,     __snake_case					:    List[Any]=False      ):
      a		:   Union[str, Any]	  =	self.config

      # find the config node of interest if it exists
      a		:   Tuple	  =	ds_key_long.split('.'      )
      for node in nodes:
        a		:   Optional[int]	  =	config
        a		:   List[str]	  =	config.get(__snake_case      )
        if config is None:
          if must_exist:
            raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""      )
          else:
            return

        # if found remove it
      if parent_config is not None:
        parent_config.pop(__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Dict      ):
      a		:   Optional[Any]	  =	self.get_value(__snake_case      )
      return False if value is None else bool(__snake_case      )







    def      lowercase_			( self					:    str		,     __snake_case					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.get_value(__snake_case      )
      return False if value is None else not bool(__snake_case      )







    def      lowercase_			( self					:    Tuple      ):
      return self._stage == 2







    def      lowercase_			( self					:    str      ):
      return self._stage == 3







    def      lowercase_			( self					:    int      ):
      return self._offload

class 				a__:







    def __init__( self					:    str		,     __snake_case					:    Any      ):
      a		:   Any	  =	engine







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    int		,     **__snake_case					:    List[Any]      ):
      # runs backpropagation and handles mixed precision
      self.engine.backward(__snake_case		,     **__snake_case      )

      # Deepspeed's `engine.step` performs the following operations:
      # - gradient accumulation check
      # - gradient clipping
      # - optimizer step
      # - zero grad
      # - checking overflow
      # - lr_scheduler step (only if engine.lr_scheduler is not None)
      self.engine.step()
      # and this plugin overrides the above calls with no-ops when Accelerate runs under
      # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
      # training loop that works transparently under many training regimes.

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    Any      ):
      super().__init__(__snake_case		,     device_placement=__snake_case		,     scaler=__snake_case      )
      a		:   List[Any]	  =	hasattr(self.optimizer		,     'overflow'      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    str=None      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







    def      lowercase_			( self					:    int      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







    @property
    def      lowercase_			( self					:    Any      ):
      if self.__has_overflow__:
        return self.optimizer.overflow
      return False

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    str		,     __snake_case					:    List[Any]		,     __snake_case					:    str      ):
      super().__init__(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Union[str, Any]      ):
      pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

class 				a__:







    def __init__( self					:    Optional[Any]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    int=0.001		,     __snake_case					:    Any=0		,     **__snake_case					:    List[Any]      ):
      a		:   Optional[Any]	  =	params
      a		:   Union[str, Any]	  =	lr
      a		:   Any	  =	weight_decay
      a		:   Any	  =	kwargs

class 				a__:







    def __init__( self					:    Any		,     __snake_case					:    int		,     __snake_case					:    int=None		,     __snake_case					:    Dict=0		,     **__snake_case					:    Optional[int]      ):
      a		:   Optional[int]	  =	optimizer
      a		:   List[str]	  =	total_num_steps
      a		:   Tuple	  =	warmup_num_steps
      a		:   Optional[Any]	  =	kwargs